Decomposition of sources of errors in seasonal streamflow forecasting over the U.S. Sunbelt

Abstract

Seasonal streamflow forecasts, contingent on climate information, can be utilized to ensure water supply for multiple uses including municipal demands, hydroelectric power generation, and for planning agricultural operations. However, uncertainties in the streamflow forecasts pose significant challenges in their utilization in real‐time operations. In this study, we systematically decompose various sources of errors in developing seasonal streamflow forecasts from two Land Surface Models (LSMs) (Noah3.2 and CLM2), which are forced with downscaled and disaggregated climate forecasts. In particular, the study quantifies the relative contributions of the sources of errors from LSMs, climate forecasts, and downscaling/disaggregation techniques in developing seasonal streamflow forecast. For this purpose, three month ahead seasonal precipitation forecasts from the ECHAM4.5 general circulation model (GCM) were statistically downscaled from 2.8° to 1/8° spatial resolution using principal component regression (PCR) and then temporally disaggregated from monthly to daily time step using kernel‐nearest neighbor (K‐NN) approach. For other climatic forcings, excluding precipitation, we considered the North American Land Data Assimilation System version 2 (NLDAS‐2) hourly climatology over the years 1979 to 2010. Then the selected LSMs were forced with precipitation forecasts and NLDAS‐2 hourly climatology to develop retrospective seasonal streamflow forecasts over a period of 20 years (1991–2010). Finally, the performance of LSMs in forecasting streamflow under different schemes was analyzed to quantify the relative contribution of various sources of errors in developing seasonal streamflow forecast. Our results indicate that the most dominant source of errors during winter and fall seasons is the errors due to ECHAM4.5 precipitation forecasts, while temporal disaggregation scheme contributes to maximum errors during summer season.

Identification of dominant source of errors in developing streamflow and groundwater projections under near‐term climate change

“…[] and Mazrooei et al . [], by upscaling the observed precipitation and temperature to the spatial resolution of BCSD (i.e., 1° × 1°) and then applying the downscaling procedure, quantile mapping, adapted by BOR. We did not estimate the downscaling error separately here, since we expect it to be very small as found by Sinha et al .…”

“…[] and Mazrooei et al . []. Though we applied the proposed equations – for four test basins, in principle it can be applied to quantify the errors in estimating the observed flows and in estimating the changes in hydrologic attributes for any given watershed.…”

“…This provides us the basic framework and a general set of equations for understanding the magnitude and direction of errors arising from the model chain. In this analysis, we considered the error due to GCM and downscaling into one single source, but one could estimate the downscaling error alone, following the approach of Sinha et al [2014] and Mazrooei et al [2015], by upscaling the observed precipitation and temperature to the spatial resolution of BCSD (i.e., 1°× 1°) and then applying the downscaling procedure, quantile mapping, adapted by BOR. We did not estimate the downscaling error separately here, since we expect it to be very small as found by Sinha et al [2014] and Mazrooei et al [2015].…”

Streamflow Simulation Using Bayesian Regression with Multivariate Linear Spline to Estimate Future Changes

“…Such pre-processing may include predictor selections, bias-correction and spatial downscaling, etc. Earlier studies have reported that the pre-processing of climate predictors can potentially include significant uncertainty in the streamflow simulation [26,41,42]. However, the current study has applied a multivariate bias-correction technique, ACCA, to remove model bias in climate predictors while preserving the interdependence between precipitation and temperature.…”

An Intercomparison of Noah Model Skills With Benefits of Assimilating SMOPS Blended and Individual Soil Moisture Retrievals

“…These suboptimal model parameters result in errors of model outputs. Additionally, LSM simulations are also prone to errors from the uncertainties existing in the forcing data and the lack of scientific understanding in model physics (Mazrooei et al, 2015;Peters-Lidard et al, 2008;Reichle & Koster, 2004).…”